基于非对称打包和FSVM的图像检索

在图像检索的相关反馈中,引入支持向量机分类方法虽可以提升图像的检索性能,但是传统的支持向量机存在正样本数少、样本非对称、过学习和弱实时性的局限。针对上述问题,提出了一种基于非对称打包的FSVM算法。该算法首先对负样本进行非对称打包处理,最后结合模糊理论与SVM实现图像检索。Corel图片集上的实验表明,当正样本数较小时,该新算法的平均查准率-查全率要优于已有算法。     

一种新的基于SVM的相关反馈图像检索算法

提出了一种新的基于支持向量机(SVM)的相关反馈图像检索算法。实验结果表明，该算法在一定程度上解决了基于SVM的相关反馈图像检索中存在的样本不足的困难，提高了系统的检索性能。     

基于F-SVMs的多模型建模方法

针对全局模型难以精确描述复杂工业过程的问题,提出一种基于模糊支持向量机(F-SVMs)的多模型(F-SVMs MM)建模方法。用模糊支持向量分类算法(F-SVC)对输入数据进行预处理,得到多模型模糊隶属度;用模糊支持回归算法(F-SVR)建立多模型(MM)估计器。应用该方法对pH中和滴定过程进行建模,仿真结果表明,F-SVMs MM跟踪性能好、泛化能力强,比USOCPN方法和标准支持向量机(SVMs)方法具有更好的性能和推广能力。     

一种主动感知的纹理图像检索方法(英文)

基于Contourlet变换和支持向量机提出了一种新的纹理图像检索方法。在这种方法中,能量和广义高斯分布参数被用做Contourlet子带图像的特征。通过这种表示,提出了由一类和二类支持向量机组成的两阶段检索算法来完成感知相似性测度。通过具有640个纹理图像的VisTex库和具有1760个纹理图像的Brodatz库证明了所提方法的有效性。实验结果表明,对于这两个纹理库,新的纹理图像检索方法的平均检索率分别达99.38%和98.07%。     

HIRBIR: A hierarchical approach to region-based image retrieval

This paper proposes a hierarchical approach to region-based image retrieval (HIRBIR) based on wavelet transform whose decomposition property is similar to human visual processing. First, automated image segmentation is performed fast in the low-low (LL) frequency subband of the wavelet domain that shows the desirable low image resolution. In the proposed system, boundaries between segmented regions are deleted to improve the robustness of region-based image retrieval against segmentation-related uncertainty. Second, a region feature vector is hierarchically represented by information in all wavelet subbands, and each feature component of a feature vector is a unified color–texture feature. Such a feature vector captures well the distinctive features (e.g., semantic texture) inside one region. Finally, employing a hierarchical feature vector, the weighted distance function for region matching is tuned meaningfully and easily, and a progressive stepwise indexing mechanism with relevance feedback is performed naturally and effectively in our system. Through experimental results and comparison with other methods, the proposed HIRBIR shows a good tradeoff between retrieval effectiveness and efficiency as well as easy implementation for region-based image retrieval.     

基于多特征融合和相关反馈的图像检索

鉴于单一视觉特征不能很好地表达图像内容,提出一种融合图像颜色、形状、纹理特征的图像检索方法。最后采用支持向量机（SVM）的相关反馈算法提高图像检索的准确度,缩小低层特征和高层语义之间的语义鸿沟。实验结果说明提出的方法具有良好的检索性能。     

基于目标区域和相关反馈的图像检索

提出了一种基于目标区域和相关反馈的图像检索方法,首先采用改进的K均值无监督分割方法将图像分割成区域,然后提取每个区域的颜色、位置、形状特征进行相似度计算;最后采用基于支持向量机（SVM）的相关反馈算法提高检索精度。实验结果表明,方法具有良好的检索性能。     

Pulse-coupled neural networks and one-class support vector machines for geometry invariant texture retrieval

The pulse-coupled neural network (PCNN) has been widely used in image processing. The outputs of PCNN represent unique features of original stimulus and are invariant to translation, rotation, scaling and distortion, which is particularly suitable for feature extraction. In this paper, PCNN and intersecting cortical model (ICM), which is a simplified version of PCNN model, are applied to extract geometrical changes of rotation and scale invariant texture features, then an one-class support vector machine based classification method is employed to train and predict the features. The experimental results show that the pulse features outperform of the classic Gabor features in aspects of both feature extraction time and retrieval accuracy, and the proposed one-class support vector machine based retrieval system is more accurate and robust to geometrical changes than the traditional Euclidean distance based system.     

基于内容的多特征融合图像检索

提出一种组合颜色、形状和空间信息的图像检索方法,用颜色块的颜色直方图表示图像的颜色特征;使用Zernike矩表示图像的形状特征,通过提取颜色块的质心、分布方差聚散度等特征得到图像的空间特征。为了进一步提高图像检索的精确度,提出一种基于支持向量机的相关反馈方法。实验结果表明,基于组合特征的图像检索方法优于基于单一特征的检索方法。     

基于SVM的图像分类研究

图像分类技术有着重要的应用前景,而且对于基于内容的图像检索的发展会有积极的推动作用。多类图像分类是图像分类中的难点,对基于SVM的多类图像分类方法进行了研究,提出在二类支持向量机的基础上构造多类分类器的方法,实验结果证明和传统方法相比,分类准确率有了较大的提高。     

基于DAGSVC的模拟电路故障字典法

针对模拟电路的故障诊断和支持向量机分类器的设计问题,讨论了一种基于有向无环图支持向量机分类器(DAGSVC)的故障字典新方法,并比较了几种支持向量机故障分类器的平均测试复杂度指标.通过对2个实际模拟滤波器的实际测试和验证表明:该方法性能要优于"1-v-r"SVC,"1-v-1"SVC等常规的故障分类器,并和聚类二叉树S...     

基于改进蚁群算法和支持向量机的网页分类研究

网页分类技术是web数据挖掘的一个重要分支,是基于自然语言处理技术和机器学习学习算法的一个典型的具体应用。基于统计学习理论和蚁群算法理论,该文提出了一种基于支持向量机和改进蚁群算法相结合的构造网页分类器的高效分类方法,实验结果证明了该方法的有效性和鲁棒性,弥补了仅利用支持向量机对于大样本训练集收敛慢的不足,具有较好的准确率和召唤率。     

A unified log-based relevance feedback scheme for image retrieval

Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named soft label support vector machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically.     

基于增量学习支持向量机的音频例子识别与检索

音频例子识别与检索的主要任务是构造一个良好的分类学习机，而在构造过程中，从含有冗余样本的训练库中选择最佳训练例子、节省学习机的训练时间是构造分类机面临的一个挑战，尤其是对含有大样本训练库音频例子的识别．由于支持向量是支持向量机中的关键例子，提出了增量学习支持向量机训练算法．在这个算法中，训练样本被分成训练子库按批次进行训练，每次训练中，只保留支持向量，去除非支持向量．与普通和减量支持向量机对比的实验表明，算法在显著减少训练时间前提下，取得了良好的识别检索正确率．     

Fuzzy SVM for content-based image retrieval: a pseudo-label support vector machine framework

Conventional relevance feedback in content-based image retrieval (CBIR) systems uses only the labeled images for learning. Image labeling, however, is a time-consuming task and users are often unwilling to label too many images during the feedback process. This gives rise to the small sample problem where learning from a small number of training samples restricts the retrieval performance. To address this problem, we propose a technique based on the concept of pseudo-labeling in order to enlarge the training data set. As the name implies, a pseudo-labeled image is an image not labeled explicitly by the users, but estimated using a fuzzy rule. Therefore, it contains a certain degree of uncertainty or fuzziness in its class information. Fuzzy support vector machine (FSVM), an extended version of SVM, takes into account the fuzzy nature of some training samples during its training. In order to exploit the advantages of pseudo-labeling, active learning and the structure of FSVM, we develop a unified framework called pseudo-label fuzzy support vector machine (PLFSVM) to perform content-based image retrieval. Experimental results based on a database of 10,000 images demonstrate the effectiveness of the proposed method.     

基于支持向量机和粗糙集的图像检索算法

研究基于支持向量机和粗糙集的相关反馈图像检索算法。利用粗糙集理论,通过对训练集的学习,构造分类规则,对支持向量机反馈后的结果再次进行处理。实验显示,与现有方法相比,该方法在图像检索的性能和时间上都有明显的改善。     

图像检索中一种支持多例查询的新方法

基于内容的图像检索的关键问题之一是高层语义和低层图像特征之间的差异,相关反馈技术是缩短这个"语义鸿沟"的有效方法。本文提出了一种新的相关反馈算法,通过分析正例图像在特征空间中的散布来构造该类图像的投影空间,该空间对应于一个语义类在特征空间中分布密集的子空间,在投影空间中计算相似图像。同时根据每次反馈的信息不断修正投影空间来提高系统的检索性能。在Corel大图像库中的实验结果表明,该算法对多例图像查询有较好的检索效果。     

基于直推式支持向量机的图像检索

为缩小图像的低层特征与高层语义之间的语义鸿沟,基于支持向量机的相关反馈机制受到越来越广泛的关注,但这种方法并没有利用未标记样本的隐含信息.为更好地利用这些信息,提出将直推式支持向量机作为反馈过程中的学习算法.通过分析其所用特征向量的特点,设计一种颜色稀疏特征,并将其与纹理特征结合作为图像描述的特征.实验结果表明该方法较令人满意,同时也说明直推式支持向量机可在文本分类以外的领域取得较好结果.     

Expert system design using wavelet and color vocabulary trees for image retrieval

A new image indexing and retrieval system for content based image retrieval (CBIR) is proposed in this paper. The characteristics (vector points) of image are computed using color (color histogram) and SOT (spatial orientation tree). The SOT defines the spatial parent-child relationship among wavelet coefficients in multi-resolution wavelet sub-bands. First the image is divided into sub-blocks and then constructed the SOT for each low pass wavelet coefficient is considered as a vector point of that particular image. Similarly the color histogram features are collected from the each sub-block. The vector points of each image are indexed using vocabulary tree. The retrieval results of the proposed method are tested on different image databases, i.e., natural image database consists of Corel 1000 (DB1), Brodatz texture image database (DB2) and MIT VisTex database (DB3). The results after being investigated show a significant improvement in terms of average precision, average recall and average retrieval rate on DB1 database and average retrieval rate on texture databases (DB2 and DB3) as compared with most of existing techniques on respective databases.     

使用基于SVM的否定概率和法的图像标注

在基于内容的图像检索中,建立图像底层视觉特征与高层语义的联系是个难题.对此提出了一种为图像提供语义标签的标注方法.先建立小规模图像库为训练集,库中每个图像标有单一的语义标签,再利用其底层特征,以SVM为子分类器,“否定概率和”法为合成方法构建基于成对耦合方式（PWC）的多类分类器,并对未标注的图像进行分类,结果以N维标注向量表示,实验表明,与一对多方式（OPC）的多类分类器及使用概率和法的PWC相比,“否定概率和”法性能更好.